New patent issued to CCIPD/BrIC labs

US Patent US 9,483,822 "CO-OCCURRENCE OF LOCAL ANISOTROPIC GRADIENT ORIENTATIONS" has been issued with Co-inventors Anant Madabhushi, Prateek Prasanna and Pallavi Tiwari.

Abstract of the invention is as follows:-

Methods, apparatus, and other embodiments associated with distinguishing disease phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe) are described. One example apparatus includes a set of logics that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating disease pathology (e.g., cancer), computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, and classifies the phenotype of the disease pathology based on the feature vector. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image.

US Patent US 9,483,822 "CO-OCCURRENCE OF LOCAL ANISOTROPIC GRADIENT ORIENTATIONS" has been issued with Co-inventors Anant Madabhushi, Prateek Prasanna and Pallavi Tiwari.

Abstract of the invention is as follows:-

Methods, apparatus, and other embodiments associated with distinguishing disease phenotypes using co-occurrence of local anisotropic gradient orientations (CoLIAGe) are described. One example apparatus includes a set of logics that acquires a radiologic image (e.g., MRI image) of a region of tissue demonstrating disease pathology (e.g., cancer), computes a gradient orientation for a pixel in the MRI image, computes a significant orientation for the pixel based on the gradient orientation, constructs a feature vector that captures a discretized entropy distribution for the image based on the significant orientation, and classifies the phenotype of the disease pathology based on the feature vector. Embodiments of example apparatus may generate and display a heatmap of entropy values for the image.